Flame Temp Report
Flame Temp Report
Flame Temp Report
by
Name: Mohammed Abdelmohsen Abdelgaber
Section: 2
Year: 4th
Dep.: Pet. Ref. & Petrochemicals Eng.
Professor: Saeid Abdallah
What is Fuzzy Logic?
Fuzzy logic is an approach to computing based on "degrees of truth" rather than the usual "true or false" (1 or
0) Boolean logic on which the modern computer is based.
The idea of fuzzy logic was first advanced by Dr. Lotfi Zadeh of the University of California at Berkeley in the
1960s. Dr. Zadeh was working on the problem of computer understanding of natural language. Natural language
(like most other activities in life and indeed the universe) is not easily translated into the absolute terms of 0
and 1. (Whether everything is ultimately describable in binary terms is a philosophical question worth pursuing,
but in practice much data we might want to feed a computer is in some state in between and so, frequently, are
the results of computing.) It may help to see fuzzy logic as the way reasoning really works and binary or Boolean
logic is simply a special case of it.
Fuzzy logic includes 0 and 1 as extreme cases of truth (or "the state of matters" or "fact") but also includes the
various states of truth in between so that, for example, the result of a comparison between two things could be
not "tall" or "short" but ".38 of tallness."
Fuzzy logic seems closer to the way our brains work. We aggregate data and form a number of partial truths
which we aggregate further into higher truths which in turn, when certain thresholds are exceeded, cause
certain further results such as motor reaction. A similar kind of process is used in neural networks, expert
systems and other artificial intelligence applications. Fuzzy logic is essential to the development of human-like
capabilities for AI, sometimes referred to as artificial general intelligence: the representation of generalized
human cognitive abilities in software so that, faced with an unfamiliar task, the AI system could find a solution.
Implementation
• It can be implemented in systems with various sizes and capabilities ranging from small micro-controllers
to large, networked, workstation-based control systems.
• It can be implemented in hardware, software, or a combination of both.
Why Fuzzy Logic?
Fuzzy logic is useful for commercial and practical purposes.
• It can control machines and consumer products.
• It may not give accurate reasoning, but acceptable reasoning.
• Fuzzy logic helps to deal with the uncertainty in engineering.
Fuzzy Logic Systems Architecture
It has four main parts as shown –
• Fuzzification Module − It transforms the system inputs, which are crisp numbers, into fuzzy sets. It splits
the input signal into five steps such as −
LP x is Large Positive
MP x is Medium Positive
S x is Small
MN x is Medium Negative
LN x is Large Negative
• Knowledge Base − It stores IF-THEN rules provided by experts.
• Inference Engine − It simulates the human reasoning process by making fuzzy inference on the inputs
and IF-THEN rules.
• Defuzzification Module − It transforms the fuzzy set obtained by the inference engine into a crisp value.
Algorithm
Step3 −
Build a set of rules into the knowledge base in the form of IF-THEN-ELSE structures.
Step 4 − Obtain fuzzy value
Fuzzy set operations perform evaluation of rules. The operations used for OR and AND are Max and Min
respectively. Combine all results of evaluation to form a final result. This result is a fuzzy value.
Step 5 − Perform defuzzification
Defuzzification is then performed according to membership function for output variable.
• Automatic Gearboxes
• Four-Wheel Steering
• Vehicle environment control
Consumer Electronic Goods
• Hi-Fi Systems
• Photocopiers
• Still and Video Cameras
• Television
Domestic Goods
• Microwave Ovens
• Refrigerators
• Toasters
• Vacuum Cleaners
• Washing Machines
Environment Control
• Air Conditioners/Dryers/Heaters
• Humidifiers
Advantages of FLSs
Working of ANNs
In the topology diagrams shown, each arrow represents a connection between two neurons and indicates the
pathway for the flow of information. Each connection has a weight, an integer number that controls the signal
between the two neurons.
If the network generates a “good or desired” output, there is no need to adjust the weights. However, if the
network generates a “poor or undesired” output or an error, then the system alters the weights in order to
improve subsequent results.
Machine Learning in ANNs
ANNs are capable of learning and they need to be trained. There are several learning strategies −
• Supervised Learning − It involves a teacher that is scholar than the ANN itself. For example, the teacher
feeds some example data about which the teacher already knows the answers.
For example, pattern recognizing. The ANN comes up with guesses while recognizing. Then the teacher
provides the ANN with the answers. The network then compares it guesses with the teacher’s “correct”
answers and makes adjustments according to errors.
• Unsupervised Learning − It is required when there is no example data set with known answers. For
example, searching for a hidden pattern. In this case, clustering i.e. dividing a set of elements into groups
according to some unknown pattern is carried out based on the existing data sets present.
• Reinforcement Learning − This strategy built on observation. The ANN makes a decision by observing its
environment. If the observation is negative, the network adjusts its weights to be able to make a
different required decision the next time.
Back Propagation Algorithm
It is the training or learning algorithm. It learns by example. If you submit to the algorithm the example of what
you want the network to do, it changes the network’s weights so that it can produce desired output for a
particular input on finishing the training.
Back Propagation networks are ideal for simple Pattern Recognition and Mapping Tasks.
Bayesian Networks (BN)
These are the graphical structures used to represent the probabilistic relationship among a set of random
variables. Bayesian networks are also called Belief Networks or Bayes Nets. BNs reason about uncertain domain.
In these networks, each node represents a random variable with specific propositions. For example, in a medical
diagnosis domain, the node Cancer represents the proposition that a patient has cancer.
The edges connecting the nodes represent probabilistic dependencies among those random variables. If out of
two nodes, one is affecting the other then they must be directly connected in the directions of the effect. The
strength of the relationship between variables is quantified by the probability associated with each node.
There is an only constraint on the arcs in a BN that you cannot return to a node simply by following directed
arcs. Hence the BNs are called Directed Acyclic Graphs (DAGs).
BNs are capable of handling multivalued variables simultaneously. The BN variables are composed of two
dimensions −
• Range of prepositions.
• Probability assigned to each of the prepositions.
Consider a finite set X = {X1, X2, …,Xn} of discrete random variables, where each variable Xi may take values
from a finite set, denoted by Val(Xi). If there is a directed link from variable Xi to variable, Xj, then variable Xi
will be a parent of variable Xj showing direct dependencies between the variables.
The structure of BN is ideal for combining prior knowledge and observed data. BN can be used to learn the
causal relationships and understand various problem domains and to predict future events, even in case of
missing data.
Building a Bayesian Network
A knowledge engineer can build a Bayesian network. There are a number of steps the knowledge engineer needs
to take while building it.
Example problem − Lung cancer. A patient has been suffering from breathlessness. He visits the doctor,
suspecting he has lung cancer. The doctor knows that barring lung cancer, there are various other possible
diseases the patient might have such as tuberculosis and bronchitis.
Step 1 - Gather Relevant Information of Problem
• Is the patient a smoker? If yes, then high chances of cancer and bronchitis.
• Is the patient exposed to air pollution? If yes, what sort of air pollution?
• Take an X-Ray positive X-ray would indicate either TB or lung cancer.
Step 2 - Identify Interesting Variables
The knowledge engineer tries to answer the questions −
• Boolean nodes − They represent propositions, taking binary values TRUE (T) and FALSE (F).
• Ordered values − A node Pollution might represent and take values from {low, medium, high} describing
degree of a patient’s exposure to pollution.
• Integral values − A node called Age might represent patient’s age with possible values from 1 to 120.
Even at this early stage, modeling choices are being made.
Possible nodes and values for the lung cancer example –